Keywords and Phrases
Cardiotocography; Computational Intelligence; Feature Extraction; Fetal Heart Rate; Machine Learning; Uterine Contractions
"In this thesis, methods for evaluating the fetal state are compared to make predictions based on Cardiotocography (CTG) data. The first part of this research is the development of an algorithm to extract features from the CTG data. A feature extraction algorithm is presented that is capable of extracting most of the features in the SISPORTO software package as well as late and variable decelerations. The resulting features are used for classification based on both U.S. National Institutes of Health (NIH) categories and umbilical cord pH data. The first experiment uses the features to classify the results into three different categories suggested by the NIH and commonly being used in practice in hospitals across the United States. In addition, the algorithms developed here were used to predict cord pH levels, the actual condition that the three NIH categories are used to attempt to measure. This thesis demonstrates the importance of machine learning in Maternal and Fetal Medicine. It provides assistance for the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the Pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology to achieve a more accurate prediction of fetal outcomes using Fetal Heartrate and Uterine Activity with accuracies of greater than 99.5% for predicting categories and greater than 70% for fetal acidosis based on pH values"--Abstract, page iii.
Long, Suzanna, 1961-
Engineering Management and Systems Engineering
M.S. in Systems Engineering
Ozark Biomedical Initiative
Phelps County Regional Medical Center
Missouri University of Science and Technology
viii, 42 pages
© 2018 Vinayaka Nagendra Harikishan Gude Divya Sampath, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Gude Divya Sampath, Vinayaka Nagendra Harikishan, "Computational intelligence methods for predicting fetal outcomes from heart rate patterns" (2018). Masters Theses. 7761.